4 papers
Scalable Fair Influence Blocking Maximization via Approximately Monotonic Submodular Optimization
Qiangpeng Fang, Jilong Shi, Xiaobin Rui +2
Influence Blocking Maximization (IBM) aims to select a positive seed set to suppress the spread of negative influence. However, existing IBM methods focus solely on maximizing bloc…
GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
Zhixiao Wang, Chaofan Zhu, Qihan Feng +3
Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations.…
GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
Chaofan Zhu, Xiaobing Rui, Zhixiao Wang
Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations.…
Efficient Approximation Algorithms for Fair Influence Maximization under Maximin Constraint
Xiaobin Rui, Qiangpeng Fang, Chen Peng +3
Fair Influence Maximization (FIM) seeks to mitigate disparities in influence across different groups and has recently garnered increasing attention. A widely adopted notion of fair…